1. Field of the Invention
The present invention relates to the field of image processing and, more particularly, to detecting regions of interests (ROIs) in images.
2. Description of the Related Art
Detecting ROIs in images is a common step in many image processing systems. Conventional digital image recognition systems (DIRSs) detect ROIs by typically using some knowledge of a prototype. Generally, an image is composed of many objects that can be defined by pixels. A group of pixels is called a region. A target is an object of interest. A prototype contains information about a type of target. A DIRS may detect a region in an image that matches the prototype.
Images may contain a lot of information. DIRSs may have different phases to more efficiently process information from an image, such as: a segmentation phase, a feature extraction phase and a classification phase. The function of the segmentation and feature extraction phases are to reduce the volume of data. In the feature extraction phase, some of the significant characteristics of each target are measured and taken together to form a vector. Thus, the image can be converted into a series of targets, each described by a vector. In the segmentation phase, each target is found and is isolated from the rest of the information in the image. The segmentation phase identifies the features to differentiate different prototypes, uses the detection algorithm to group pixels with similar features, and uses a merging algorithm to group regions of the image. Because these issues may impact the efficiency and effectiveness of a DIRS, the segmentation phase can affect the functionality of the entire process.
The algorithms used in the segmentation phase are typically applied to an entire image to reduce the data dimension for subsequent processing purposes. Pixels with similar optical characteristics, such as color and intensity, are grouped together and separated from the others. Particular regions can then be chosen based upon features such as size and shape, and forwarded to successive processing units.
Several problems hinder the segmentation process. First, because of the characteristics of the image, such as luminance, noise level, ROI position uncertainty, etc., it is difficult to determine the critical parameters for segmentation at different positions. Secondly, different objects in the image have different and distinctive features. Even when the pixels of the objects are accurately grouped and separated, other problems remain. For example, several objects may overlap one other, or one object may be broken into pieces and therefore, different regions may correspond to the same object.
Sophisticated algorithms have been proposed to address some of these segmentation issues. However, the computational complexity associated with these algorithms may prohibit them from use in real-time applications. One solution is to quickly detect regions that may correspond to objects, and then apply dedicated algorithms to those regions. Another solution is to first transform the image, and then cluster the image to detect the ROIs. Yet another solution is to first threshold the image, and then take every “blob” as one region.
All of these algorithms detect ROIs by grouping pixels with similar features. However, because the objects may be connected or broken, and portions of an object may be very close to the background, a ROI may correspond to several objects, or a portion of an object. These conventional solutions for segmentation focus on detecting regions of similar optical characteristics, each of which may be only part of one object, or a concatenation of several objects. Thus, these solutions may result in inaccurate, incomplete, or too general results in detecting ROIs. In addition, they are often computationally complex and usually do not work well with low quality images. Thus, there is a need for a method to detect ROIs that is more accurate, efficient, and faster than conventional methods.